WORKERS AHEAD!
You are viewing the development documentation for the Apereo CAS server. The functionality presented here is not officially released yet. This is a work in progress and will be continually updated as development moves forward. You are most encouraged to test the changes presented.
Throttling Authentication Attempts
Capacity Throttling
CAS is able to support request rate-limiting based on the token-bucket algorithm, via the Bucket4j project. This
means that authentication requests that reach a certain configurable capacity within a time window may either be blocked or throttled to slow down. This is done to
protect the system from overloading, allowing you to introduce a scenario to allow CAS 120 authentication requests per minute with a refill rate of 10 requests per
second that would continually increase in the capacity bucket. Please note that the bucket allocation strategy is specific to the client IP address.
Enable the following module in your configuration overlay:
1
2
3
4
5
<dependency>
<groupId>org.apereo.cas</groupId>
<artifactId>cas-server-support-throttle-bucket4j</artifactId>
<version>${cas.version}</version>
</dependency>
1
implementation "org.apereo.cas:cas-server-support-throttle-bucket4j:${project.'cas.version'}"
1
2
3
4
5
6
7
8
9
dependencyManagement {
imports {
mavenBom "org.apereo.cas:cas-server-support-bom:${project.'cas.version'}"
}
}
dependencies {
implementation "org.apereo.cas:cas-server-support-throttle-bucket4j"
}
1
2
3
4
5
6
dependencies {
implementation enforcedPlatform("org.apereo.cas:cas-server-support-bom:${project.'cas.version'}")
implementation platform(org.springframework.boot.gradle.plugin.SpringBootPlugin.BOM_COORDINATES)
implementation "org.apereo.cas:cas-server-support-throttle-bucket4j"
}
The following settings and properties are available from the CAS configuration catalog:
cas.authn.throttle.bucket4j.bandwidth[0].capacity=120
Number of tokens/requests that can be used within the time window. |
cas.authn.throttle.bucket4j.bandwidth[0].duration=PT60S
Time window in which capacity can be allowed. This settings supports the
|
cas.authn.throttle.bucket4j.bandwidth[0].initial-tokens=
By default initial size of bucket equals to capacity. But sometimes, you may want to have lesser initial size, for example for case of cold start in order to prevent denial of service. |
cas.authn.throttle.bucket4j.bandwidth[0].refill-count=10
The number of tokens that should be used to refill the bucket given the specified refill duration. |
cas.authn.throttle.bucket4j.bandwidth[0].refill-duration=PT30S
Duration to use to refill the bucket. This settings supports the
|
cas.authn.throttle.bucket4j.bandwidth[0].refill-strategy=GREEDY
Describes how the bucket should be refilled. Specifies the speed of tokens regeneration. Available values are as follows:
|
cas.authn.throttle.bucket4j.bandwidth=
Describe the available bandwidth and the overall limitations. Multiple bandwidths allow for different policies per unit of measure. (i.e. allows 1000 tokens per 1 minute, but not often then 50 tokens per 1 second). |
cas.authn.throttle.bucket4j.blocking=true
Whether the request should block until capacity becomes available. Consume a token from the token bucket. If a token is not available this will block until the refill adds one to the bucket. |
cas.authn.throttle.bucket4j.enabled=true
Decide whether bucket4j functionality should be enabled. |
Configuration Metadata
The collection of configuration properties listed in this section are automatically generated from the CAS source and components that contain the actual field definitions, types, descriptions, modules, etc. This metadata may not always be 100% accurate, or could be lacking details and sufficient explanations.
Be Selective
This section is meant as a guide only. Do NOT copy/paste the entire collection of settings into your CAS configuration; rather pick only the properties that you need. Do NOT enable settings unless you are certain of their purpose and do NOT copy settings into your configuration only to keep them as reference. All these ideas lead to upgrade headaches, maintenance nightmares and premature aging.
YAGNI
Note that for nearly ALL use cases, declaring and configuring properties listed here is sufficient. You should NOT have to explicitly massage a CAS XML/Java/etc configuration file to design an authentication handler, create attribute release policies, etc. CAS at runtime will auto-configure all required changes for you. If you are unsure about the meaning of a given CAS setting, do NOT turn it on without hesitation. Review the codebase or better yet, ask questions to clarify the intended behavior.
Naming Convention
Property names can be specified in very relaxed terms. For instance cas.someProperty, cas.some-property, cas.some_property are all valid names. While all
forms are accepted by CAS, there are certain components (in CAS and other frameworks used) whose activation at runtime is conditional on a property value, where
this property is required to have been specified in CAS configuration using kebab case. This is both true for properties that are owned by CAS as well as those
that might be presented to the system via an external library or framework such as Spring Boot, etc.
When possible, properties should be stored in lower-case kebab format, such as cas.property-name=value.
The only possible exception to this rule is when naming actuator endpoints; The name of the
actuator endpoints (i.e. ssoSessions) MUST remain in camelCase mode.
Settings and properties that are controlled by the CAS platform directly always begin with the prefix cas. All other settings are controlled and provided
to CAS via other underlying frameworks and may have their own schemas and syntax. BE CAREFUL with
the distinction. Unrecognized properties are rejected by CAS and/or frameworks upon which CAS depends. This means if you somehow misspell a property definition
or fail to adhere to the dot-notation syntax and such, your setting is entirely refused by CAS and likely the feature it controls will never be activated in the
way you intend.
Validation
Configuration properties are automatically validated on CAS startup to report issues with configuration binding, specially if defined CAS settings cannot be
recognized or validated by the configuration schema. The validation process is on by default and can be skipped on startup using a special system
property SKIP_CONFIG_VALIDATION that should be set to true. Additional validation processes are also handled
via Configuration Metadata and property migrations applied automatically on
startup by Spring Boot and family.
Indexed Settings
CAS settings able to accept multiple values are typically documented with an index, such as cas.some.setting[0]=value. The index [0] is meant to be
incremented by the adopter to allow for distinct multiple configuration blocks.
Failure Throttling
CAS provides a facility for limiting failed login attempts to support password guessing and related abuse scenarios. A couple strategies are provided for tracking failed attempts:
- Source IP - Limit successive failed logins against any username from the same IP address.
- Source IP and username - Limit successive failed logins against a particular user from the same IP address.
All login throttling components that ship with CAS limit successive failed login attempts that exceed a threshold rate in failures per second. The following properties are provided to define the failure rate.
-
failureRangeInSeconds- Period of time in seconds during which the threshold applies. -
failureThreshold- Number of failed login attempts permitted in the above period.
A failure rate of more than 1 per 3 seconds is indicative of an automated authentication attempt, which is a reasonable basis for throttling policy. Regardless of policy care should be taken to weigh security against access; overly restrictive policies may prevent legitimate authentication attempts.
The failure threshold rate is calculated as: failureThreshold / failureRangeInSeconds. For instance,
the failure rate for the above scenario would be 0.333333. An authentication attempt may be considered throttled
if the request submission rate (calculated as the difference between the current date and the last submission date) exceeds
the failure threshold rate.
Enable the following module in your configuration overlay:
1
2
3
4
5
<dependency>
<groupId>org.apereo.cas</groupId>
<artifactId>cas-server-support-throttle</artifactId>
<version>${cas.version}</version>
</dependency>
1
implementation "org.apereo.cas:cas-server-support-throttle:${project.'cas.version'}"
1
2
3
4
5
6
7
8
9
dependencyManagement {
imports {
mavenBom "org.apereo.cas:cas-server-support-bom:${project.'cas.version'}"
}
}
dependencies {
implementation "org.apereo.cas:cas-server-support-throttle"
}
1
2
3
4
5
6
dependencies {
implementation enforcedPlatform("org.apereo.cas:cas-server-support-bom:${project.'cas.version'}")
implementation platform(org.springframework.boot.gradle.plugin.SpringBootPlugin.BOM_COORDINATES)
implementation "org.apereo.cas:cas-server-support-throttle"
}
Configuration
The following settings and properties are available from the CAS configuration catalog:
cas.authn.throttle.hazelcast.cluster.core.instance-name=
The instance name. This setting supports the Spring Expression Language. |
cas.authn.throttle.schedule.enabled=true
Whether scheduler should be enabled to schedule the job to run. |
cas.authn.throttle.schedule.enabled-on-host=.*
Overrides This settings supports regular expression patterns. [?]. |
cas.authn.throttle.schedule.repeat-interval=PT2M
String representation of a repeat interval of re-loading data for a data store implementation. This is the timeout between consecutive job’s executions. This settings supports the
|
cas.authn.throttle.schedule.start-delay=PT15S
String representation of a start delay of loading data for a data store implementation. This is the delay between scheduler startup and first job’s execution This settings supports the
|
cas.authn.throttle.hazelcast.cluster.core.async-backup-count=0
Hazelcast supports both synchronous and asynchronous backups. By default, backup operations are synchronous. In this case, backup operations block operations until backups are successfully copied to backup members (or deleted from backup members in case of remove) and acknowledgements are received. Therefore, backups are updated before a put operation is completed, provided that the cluster is stable. Asynchronous backups, on the other hand, do not block operations. They are fire and forget and do not require acknowledgements; the backup operations are performed at some point in time. |
cas.authn.throttle.hazelcast.cluster.core.async-fillup=true
Used when replication is turned on with
|
cas.authn.throttle.hazelcast.cluster.core.backup-count=1
To provide data safety, Hazelcast allows you to specify the number of backup copies you want to have. That way, data on a cluster member will be copied onto other member(s). To create synchronous backups, select the number of backup copies. When this count is 1, a map entry will have its backup on one other member in the cluster. If you set it to 2, then a map entry will have its backup on two other members. You can set it to 0 if you do not want your entries to be backed up, e.g., if performance is more important than backing up. The maximum value for the backup count is 6. Sync backup operations have a blocking cost which may lead to latency issues. |
cas.authn.throttle.hazelcast.cluster.core.cp-member-count=0
CP Subsystem is a component of a Hazelcast cluster that builds a strongly consistent layer for a set of distributed data structures. Its data structures are CP with respect to the CAP principle, i.e., they always maintain linearizability and prefer consistency over availability during network partitions. Besides network partitions, CP Subsystem withstands server and client failures. All members of a Hazelcast cluster do not necessarily take part in CP Subsystem. The number of Hazelcast members that take part in CP Subsystem is specified here. CP Subsystem must have at least 3 CP members. |
cas.authn.throttle.hazelcast.cluster.core.eviction-policy=LRU
Hazelcast supports policy-based eviction for distributed maps. Currently supported policies are LRU (Least Recently Used) and LFU (Least Frequently Used) and NONE. See this for more info. |
cas.authn.throttle.hazelcast.cluster.core.logging-type=slf4j
Hazelcast has a flexible logging configuration and doesn't depend on any logging framework except JDK logging. It has in-built adaptors for a number of logging frameworks and also supports custom loggers by providing logging interfaces. To use built-in adaptors you should set this setting to one of predefined types below.
|
cas.authn.throttle.hazelcast.cluster.core.map-merge-policy=PUT_IF_ABSENT
Define how data items in Hazelcast maps are merged together from source to destination. By default, merges map entries from source to destination if they don't exist in the destination map. Accepted values are:
|
cas.authn.throttle.hazelcast.cluster.core.max-no-heartbeat-seconds=300
Max timeout of heartbeat in seconds for a node to assume it is dead. |
cas.authn.throttle.hazelcast.cluster.core.max-size=85
Sets the maximum size of the map. |
cas.authn.throttle.hazelcast.cluster.core.max-size-policy=USED_HEAP_PERCENTAGE
|
cas.authn.throttle.hazelcast.cluster.core.partition-member-group-type=
With
|
cas.authn.throttle.hazelcast.cluster.core.replicated=false
A Replicated Map is a distributed key-value data structure where the data is replicated to all members in the cluster. It provides full replication of entries to all members for high speed access. A Replicated Map does not partition data (it does not spread data to different cluster members); instead, it replicates the data to all members. Replication leads to higher memory consumption. However, a Replicated Map has faster read and write access since the data is available on all members. Writes could take place on local/remote members in order to provide write-order, eventually being replicated to all other members. Replicated Map uses the internal partition system of Hazelcast in order to serialize updates happening on the same key at the same time. This happens by sending updates of the same key to the same Hazelcast member in the cluster. Due to the asynchronous nature of replication, a Hazelcast member could die before successfully replicating a "write" operation to other members after sending the "write completed" response to its caller during the write process. In this scenario, Hazelcast’s internal partition system promotes one of the replicas of the partition as the primary one. The new primary partition does not have the latest "write" since the dead member could not successfully replicate the update. |
cas.authn.throttle.hazelcast.cluster.core.timeout=5
Connection timeout in seconds for the TCP/IP config and members joining the cluster. |
cas.authn.throttle.hazelcast.core.enable-compression=false
Enables compression when default java serialization is used. |
cas.authn.throttle.hazelcast.core.enable-jet=true
Enable Jet configuration/service on the hazelcast instance. Hazelcast Jet is a distributed batch and stream processing system that can do stateful computations over massive amounts of data with consistent low latency. Jet service is required when executing SQL queries with the SQL service. |
cas.authn.throttle.hazelcast.core.enable-management-center-scripting=true
Enables scripting from Management Center. |
cas.authn.throttle.hazelcast.core.license-key=
Hazelcast enterprise license key. |
cas.authn.throttle.core.app-code=CAS
Application code used to identify this application in the audit logs. |
cas.authn.throttle.core.username-parameter=
Username parameter to use in order to extract the username from the request. |
cas.authn.throttle.failure.code=AUTHENTICATION_FAILED
Failure code to record in the audit log. Generally this indicates an authentication failure event. |
cas.authn.throttle.failure.range-seconds=-1
Period of time in seconds during which the threshold applies. |
cas.authn.throttle.failure.threshold=-1
Number of failed login attempts permitted in the given period. All login throttling components that ship with CAS limit successive failed login attempts that exceed a threshold rate in failures per second. |
cas.authn.throttle.failure.throttle-window-seconds=0
Indicate the number of seconds the account should remain in a locked/throttled state before it can be released to continue again. If no value is specified, the failure threshold and rate that is calculated would hold. This settings supports the
|
Configuration Metadata
The collection of configuration properties listed in this section are automatically generated from the CAS source and components that contain the actual field definitions, types, descriptions, modules, etc. This metadata may not always be 100% accurate, or could be lacking details and sufficient explanations.
Be Selective
This section is meant as a guide only. Do NOT copy/paste the entire collection of settings into your CAS configuration; rather pick only the properties that you need. Do NOT enable settings unless you are certain of their purpose and do NOT copy settings into your configuration only to keep them as reference. All these ideas lead to upgrade headaches, maintenance nightmares and premature aging.
YAGNI
Note that for nearly ALL use cases, declaring and configuring properties listed here is sufficient. You should NOT have to explicitly massage a CAS XML/Java/etc configuration file to design an authentication handler, create attribute release policies, etc. CAS at runtime will auto-configure all required changes for you. If you are unsure about the meaning of a given CAS setting, do NOT turn it on without hesitation. Review the codebase or better yet, ask questions to clarify the intended behavior.
Naming Convention
Property names can be specified in very relaxed terms. For instance cas.someProperty, cas.some-property, cas.some_property are all valid names. While all
forms are accepted by CAS, there are certain components (in CAS and other frameworks used) whose activation at runtime is conditional on a property value, where
this property is required to have been specified in CAS configuration using kebab case. This is both true for properties that are owned by CAS as well as those
that might be presented to the system via an external library or framework such as Spring Boot, etc.
When possible, properties should be stored in lower-case kebab format, such as cas.property-name=value.
The only possible exception to this rule is when naming actuator endpoints; The name of the
actuator endpoints (i.e. ssoSessions) MUST remain in camelCase mode.
Settings and properties that are controlled by the CAS platform directly always begin with the prefix cas. All other settings are controlled and provided
to CAS via other underlying frameworks and may have their own schemas and syntax. BE CAREFUL with
the distinction. Unrecognized properties are rejected by CAS and/or frameworks upon which CAS depends. This means if you somehow misspell a property definition
or fail to adhere to the dot-notation syntax and such, your setting is entirely refused by CAS and likely the feature it controls will never be activated in the
way you intend.
Validation
Configuration properties are automatically validated on CAS startup to report issues with configuration binding, specially if defined CAS settings cannot be
recognized or validated by the configuration schema. The validation process is on by default and can be skipped on startup using a special system
property SKIP_CONFIG_VALIDATION that should be set to true. Additional validation processes are also handled
via Configuration Metadata and property migrations applied automatically on
startup by Spring Boot and family.
Indexed Settings
CAS settings able to accept multiple values are typically documented with an index, such as cas.some.setting[0]=value. The index [0] is meant to be
incremented by the adopter to allow for distinct multiple configuration blocks.
Actuator Endpoints
The following endpoints are provided by CAS:
Get throttled authentication records.
List
|
|
org.apereo.cas.web.support.ThrottledSubmissionHandlerEndpoint
|
Throttling Strategies
The following throttling strategies are offered by CAS.
| Storage | Description |
|---|---|
| IP Address | Uses a memory map to prevent successive failed login attempts from the same IP address. |
| IP Address and Username | Uses a memory map to prevent successive failed login attempts for a username from the same IP address. |
| JDBC | See this guide. |
| MongoDb | See this guide. |
| Redis | See this guide. |
| Hazelcast | See this guide. |
High Availability
All of the throttling components are suitable for a CAS deployment that satisfies the recommended HA architecture. In particular deployments with multiple CAS nodes behind a load balancer configured with session affinity can use either in-memory or inspektr components. It is instructive to discuss the rationale. Since load balancer session affinity is determined by source IP address, which is the same criterion by which throttle policy is applied, an attacker from a fixed location should be bound to the same CAS server node for successive authentication attempts. A distributed attack, on the other hand, where successive request would be routed indeterminately, would cause haphazard tracking for in-memory CAS components since attempts would be split across N systems. However, since the source varies, accurate accounting would be pointless since the throttling components themselves assume a constant source IP for tracking purposes. The login throttling components are not sufficient for detecting or preventing a distributed password brute force attack.